{"title":"约束满足中的陈述启发式","authors":"E. Teppan, G. Friedrich","doi":"10.1109/ICTAI.2013.150","DOIUrl":null,"url":null,"abstract":"Constraint Satisfaction Problems (CSPs) have the big advantage of a succinct, declarative and easy to understand representation form. Unfortunately, solving CSPs is NP-complete in the general case. In order to cope with this, common CSP frameworks offer the possibility to use different built-in heuristics. However, the provided built-in heuristics are often not suitable to significantly boost solution calculation. Also the facilities for expressing domain-specific heuristics in a declarative manner within the CSP framework are typically very limited (e.g. by defining a static variable selection order)and thus are often not applicable. As a consequence such domain-specific heuristics are often implemented by means of custom propagators or custom constraints (e.g. a special constraint for bin packing problems) forcing domain experts and knowledge engineers to leave the declarative world and implement the heuristics in a procedural manner. In this paper we propose a new declarative language for expressing domain specific heuristics for CSPs which can be easily integrated in every CSP framework. We also describe a prototype implementation within a state-of-the-art CSP solver and present proof of concept results on real world configuration problem instances.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Declarative Heuristics in Constraint Satisfaction\",\"authors\":\"E. Teppan, G. Friedrich\",\"doi\":\"10.1109/ICTAI.2013.150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Constraint Satisfaction Problems (CSPs) have the big advantage of a succinct, declarative and easy to understand representation form. Unfortunately, solving CSPs is NP-complete in the general case. In order to cope with this, common CSP frameworks offer the possibility to use different built-in heuristics. However, the provided built-in heuristics are often not suitable to significantly boost solution calculation. Also the facilities for expressing domain-specific heuristics in a declarative manner within the CSP framework are typically very limited (e.g. by defining a static variable selection order)and thus are often not applicable. As a consequence such domain-specific heuristics are often implemented by means of custom propagators or custom constraints (e.g. a special constraint for bin packing problems) forcing domain experts and knowledge engineers to leave the declarative world and implement the heuristics in a procedural manner. In this paper we propose a new declarative language for expressing domain specific heuristics for CSPs which can be easily integrated in every CSP framework. We also describe a prototype implementation within a state-of-the-art CSP solver and present proof of concept results on real world configuration problem instances.\",\"PeriodicalId\":140309,\"journal\":{\"name\":\"2013 IEEE 25th International Conference on Tools with Artificial Intelligence\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 25th International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2013.150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2013.150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Constraint Satisfaction Problems (CSPs) have the big advantage of a succinct, declarative and easy to understand representation form. Unfortunately, solving CSPs is NP-complete in the general case. In order to cope with this, common CSP frameworks offer the possibility to use different built-in heuristics. However, the provided built-in heuristics are often not suitable to significantly boost solution calculation. Also the facilities for expressing domain-specific heuristics in a declarative manner within the CSP framework are typically very limited (e.g. by defining a static variable selection order)and thus are often not applicable. As a consequence such domain-specific heuristics are often implemented by means of custom propagators or custom constraints (e.g. a special constraint for bin packing problems) forcing domain experts and knowledge engineers to leave the declarative world and implement the heuristics in a procedural manner. In this paper we propose a new declarative language for expressing domain specific heuristics for CSPs which can be easily integrated in every CSP framework. We also describe a prototype implementation within a state-of-the-art CSP solver and present proof of concept results on real world configuration problem instances.